Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 66556

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 73004

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-23T21:53:30+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

病床データ

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`) %>% 
  dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}

　

対策ダッシュボード

NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。

"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>% 
  readr::read_csv(guess_max = 12500) %>% 
  dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
                confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
                `入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
                `確認中`) %>% 
  dplyr::mutate(date = lubridate::as_date(date)) %>% 
  dplyr::mutate_if(is.numeric, .funs = as.integer)

　

新型コロナ関連ニュース

新型コロナ関連のニュース

news <- "https://gist.githubusercontent.com/k-metrics/76fea197fa32466a2f99ff59f721b98a/raw/4c971a6cde2033e458525b793e832c4a87cbae2d/covid19_news.csv" %>% 
    readr::read_csv() %>% 
  dplyr::filter(area == "日本")
news

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	136384
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	134555	0
dateAnnounced	0	1.00	10	10	0	300	0
gender	33308	0.76	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	132266	0.03	8	23	0	8	0
notes	73055	0.46	1	270	0	60388	1
mhlwPatientNumber	135935	0.00	1	11	0	434	0
prefecturePatientNumber	29615	0.78	5	20	0	106760	0
prefectureSourceURL	104993	0.23	5	224	0	3453	0
residence	41453	0.70	1	38	0	1428	0
sourceURL	1170	0.99	1	239	0	8665	0
relatedPatients	124533	0.09	2	259	0	7067	0
knownCluster	133857	0.02	3	88	0	234	0
detectedCityTown	108324	0.21	2	22	0	667	0
cityPrefectureNumber	108623	0.20	1	34	0	27752	2
citySourceURL	124347	0.09	9	317	0	3676	0
deceasedDate	134425	0.01	10	10	0	250	0
deceasedReportedDate	135164	0.01	10	62	0	207	0
deathSourceURL	135309	0.01	14	123	0	656	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 134554, FAL: 1830
charterFlightPassenger	136370	0	1.00	TRU: 14
cruisePassengerDisembarked	136373	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	28.46	24.61	-1	0	20	50	100	▇▇▅▃▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	133104
Number of columns	18
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	8	0	133104	0
knownCluster	130627	0.02	3	88	0	231	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-23	2020-08-29	297
deceasedDate	132725	0	2020-02-13	2020-11-19	2020-05-08	150
deceasedReportedDate	132775	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	31620	0.76	FALSE	2	M: 56841, F: 44643
patientStatus	130593	0.02	FALSE	8	Hos: 1246, Dec: 371, Hom: 315, Dis: 276
ageBracket	0	1.00	FALSE	13	-1: 31705, 20: 27451, 30: 17591, 40: 14761
pcode	0	1.00	FALSE	47	13: 38060, 27: 17931, 14: 11432, 23: 8925
pref	0	1.00	FALSE	47	東京都: 38060, 大阪府: 17931, 神奈川: 11432, 愛知県: 8925
region	0	1.00	FALSE	8	関東地: 66556, 近畿地: 28162, 中部地: 13966, 九州地: 12349
広域圏	11378	0.91	FALSE	8	首都圏: 66883, 近畿圏: 27431, 中部圏: 12472, 九州圏: 8329
通俗的区分	0	1.00	FALSE	11	関東: 66556, 関西: 27431, 東海: 11855, 九州: 8329
fct_pref	0	1.00	FALSE	47	Tok: 38060, Osa: 17931, Kan: 11432, Aic: 8925

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 133104
charterFlightPassenger	133097	0	1.00	TRU: 7
cruisePassengerDisembarked	133093	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 130627, TRU: 2477

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.05）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.05）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100

emergency <- news %>% 
  dplyr::filter(category == "緊急事態")

goto <- news %>% 
  dplyr::filter(category == "GoTo")

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    # ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = emergency,
    #                     colour = "dark blue", linetype = "dashed", size = 0.15) + 
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = goto,
                        colour = "magenta", linetype = "dashed", size = 0.15) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      fill = "dark gray", alpha = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    # ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2500, label = news),
    #                           label.size = 0.05, data = emergency) + 
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2500, label = news),
                              label.size = 0.05, data = goto, colour = "magenta") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

死亡者の日次集計

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_prefs %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

年代別

x_by_age <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9"))
x_by_age

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(rate = round((n / sum(n) * 100), 1))

　

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = ageBracket, values_from = n)

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() +
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) +
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

都道府県別

g_age_by_pref <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::group_by(pref, ageBracket, cluster) %>%
  # dplyr::group_by(pref, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") +
    # ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref

g_age_by_pref + 
  ggplot2::facet_grid(~ cluster)

g_age_by_pref_wo <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(pref, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref_wo

g_age_by_pref_wo + 
  ggplot2::facet_grid(~ cluster) + 
  ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime))

　

Visualize

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 233.7388 246.2060 258.0195 274.0600 250.8650 257.4728 247.7903 256.4522
##  [9] 258.2759 258.9549 259.5509 260.0966 260.5963 261.0539
## 
## $北海道地方$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 222.0240 215.8225
## 46.00000 232.4460 225.1619
## 46.14286 240.3063 230.9295
## 46.28571 255.2812 245.3402
## 46.42857 230.9041 220.3375
## 46.57143 236.2350 224.9924
## 46.71429 225.2004 213.2420
## 46.85714 230.3103 216.4716
## 47.00000 229.6795 214.5414
## 47.14286 227.2763 210.5067
## 47.28571 225.7381 207.8388
## 47.42857 224.1032 205.0495
## 47.57143 222.3914 202.1670
## 47.71429 220.6192 199.2144
## 
## $北海道地方$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 245.4537 251.6552
## 46.00000 259.9661 267.2502
## 46.14286 275.7327 285.1094
## 46.28571 292.8389 302.7798
## 46.42857 270.8259 281.3925
## 46.57143 278.7105 289.9531
## 46.71429 270.3802 282.3386
## 46.85714 282.5942 296.4329
## 47.00000 286.8723 302.0104
## 47.14286 290.6334 307.4031
## 47.28571 293.3636 311.2630
## 47.42857 296.0900 315.1437
## 47.57143 298.8012 319.0257
## 47.71429 301.4887 322.8935
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 25.99257 30.01299 31.65197 33.81496 32.61370 31.51093 28.96006 28.09792
##  [9] 27.58289 28.86194 29.99520 31.39857 31.60227 31.36104
## 
## $東北地方$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 19.05453 15.38176
## 46.00000 22.82771 19.02405
## 46.14286 24.29907 20.40668
## 46.28571 26.45283 22.55555
## 46.42857 25.20179 21.27817
## 46.57143 24.03929 20.08404
## 46.71429 21.22211 17.12589
## 46.85714 20.03701 15.76983
## 47.00000 19.08640 14.58863
## 47.14286 20.07941 15.43021
## 47.28571 21.00073 16.23934
## 47.42857 22.30658 17.49357
## 47.57143 22.42603 17.56842
## 47.71429 22.10755 17.20905
## 
## $東北地方$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 32.93060 36.60338
## 46.00000 37.19828 41.00194
## 46.14286 39.00488 42.89727
## 46.28571 41.17708 45.07436
## 46.42857 40.02561 43.94924
## 46.57143 38.98257 42.93782
## 46.71429 36.69801 40.79424
## 46.85714 36.15882 40.42601
## 47.00000 36.07938 40.57715
## 47.14286 37.64448 42.29367
## 47.28571 38.98967 43.75105
## 47.42857 40.49057 45.30358
## 47.57143 40.77851 45.63612
## 47.71429 40.61453 45.51303
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1]  862.8773 1030.8008 1093.3280 1070.7702 1130.5271  891.2728  697.0286
##  [8]  949.0610 1114.2788 1175.0872 1146.7607 1206.2558  961.7456  766.3748
## 
## $関東地方$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%      95%
## 45.85714  795.0501 759.1445
## 46.00000  944.8099 899.2891
## 46.14286 1000.6008 951.5140
## 46.28571  971.8601 919.5002
## 46.42857 1023.4213 966.7230
## 46.57143  780.6816 722.1381
## 46.71429  579.9122 517.9146
## 46.85714  816.0389 745.6212
## 47.00000  967.9620 890.5066
## 47.14286 1021.6382 940.4073
## 47.28571  986.4892 901.6465
## 47.42857 1038.6107 949.8648
## 47.57143  789.5068 698.3291
## 47.71429  588.0791 493.6951
## 
## $関東地方$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%       95%
## 45.85714  930.7046  966.6101
## 46.00000 1116.7917 1162.3126
## 46.14286 1186.0551 1235.1419
## 46.28571 1169.6804 1222.0403
## 46.42857 1237.6328 1294.3312
## 46.57143 1001.8640 1060.4074
## 46.71429  814.1449  876.1426
## 46.85714 1082.0831 1152.5007
## 47.00000 1260.5956 1338.0510
## 47.14286 1328.5362 1409.7672
## 47.28571 1307.0323 1391.8749
## 47.42857 1373.9009 1462.6468
## 47.57143 1133.9844 1225.1621
## 47.71429  944.6704 1039.0544
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 272.1868 324.5264 343.6083 337.1772 348.6528 278.3842 255.4534 307.2488
##  [9] 338.7312 351.4502 346.6221 354.5905 310.3570 297.0484
## 
## $中部地方$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 250.1572 238.4954
## 46.00000 296.5285 281.7073
## 46.14286 311.4122 294.3686
## 46.28571 302.1495 283.6069
## 46.42857 310.9716 291.0244
## 46.57143 238.6302 217.5857
## 46.71429 213.5564 191.3775
## 46.85714 257.9871 231.9095
## 47.00000 284.0766 255.1442
## 47.14286 292.5287 261.3375
## 47.28571 283.9201 250.7276
## 47.42857 288.5455 253.5833
## 47.57143 241.1400 204.4987
## 47.71429 224.9061 186.7163
## 
## $中部地方$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 294.2164 305.8782
## 46.00000 352.5243 367.3454
## 46.14286 375.8045 392.8481
## 46.28571 372.2050 390.7476
## 46.42857 386.3339 406.2811
## 46.57143 318.1382 339.1827
## 46.71429 297.3503 319.5292
## 46.85714 356.5105 382.5881
## 47.00000 393.3858 422.3182
## 47.14286 410.3718 441.5630
## 47.28571 409.3241 442.5166
## 47.42857 420.6356 455.5977
## 47.57143 379.5741 416.2153
## 47.71429 369.1908 407.3806
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1]  687.5518  726.3644  776.7936  805.4051  866.2461  868.1244  606.8927
##  [8]  890.1574  929.4365  988.2674 1015.2439 1082.0004 1093.2035  821.3172
## 
## $近畿地方$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 649.5976 629.5059
## 46.00000 682.1808 658.7914
## 46.14286 728.7212 703.2733
## 46.28571 753.7359 726.3838
## 46.42857 811.2146 782.0826
## 46.57143 809.9244 779.1152
## 46.71429 545.6881 513.2884
## 46.85714 815.1830 775.4939
## 47.00000 847.3343 803.8720
## 47.14286 900.4726 853.9969
## 47.28571 922.1039 872.7986
## 47.42857 983.8057 931.8245
## 47.57143 990.2018 935.6760
## 47.71429 713.7231 656.7662
## 
## $近畿地方$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%       95%
## 45.85714  725.5059  745.5977
## 46.00000  770.5480  793.9374
## 46.14286  824.8659  850.3139
## 46.28571  857.0744  884.4264
## 46.42857  921.2777  950.4096
## 46.57143  926.3243  957.1335
## 46.71429  668.0972  700.4969
## 46.85714  965.1318 1004.8209
## 47.00000 1011.5387 1055.0009
## 47.14286 1076.0621 1122.5378
## 47.28571 1108.3839 1157.6892
## 47.42857 1180.1951 1232.1763
## 47.57143 1196.2051 1250.7309
## 47.71429  928.9113  985.8682
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 31.79732 32.59840 32.59840 32.59840 32.59840 32.59840 32.59840 32.59840
##  [9] 32.59840 32.59840 32.59840 32.59840 32.59840 32.59840
## 
## $中国地方$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 22.32278 17.30727
## 46.00000 22.54009 17.21554
## 46.14286 22.25596 16.78100
## 46.28571 21.97942 16.35807
## 46.42857 21.70991 15.94589
## 46.57143 21.44691 15.54366
## 46.71429 21.18996 15.15070
## 46.85714 20.93868 14.76640
## 47.00000 20.69271 14.39021
## 47.14286 20.45171 14.02164
## 47.28571 20.21540 13.66023
## 47.42857 19.98352 13.30560
## 47.57143 19.75582 12.95737
## 47.71429 19.53209 12.61521
## 
## $中国地方$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 41.27186 46.28738
## 46.00000 42.65672 47.98126
## 46.14286 42.94085 48.41581
## 46.28571 43.21739 48.83873
## 46.42857 43.48690 49.25092
## 46.57143 43.74990 49.65315
## 46.71429 44.00684 50.04611
## 46.85714 44.25812 50.43041
## 47.00000 44.50410 50.80660
## 47.14286 44.74510 51.17517
## 47.28571 44.98141 51.53657
## 47.42857 45.21329 51.89121
## 47.57143 45.44099 52.23944
## 47.71429 45.66471 52.58160
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 24.37091 23.43940 23.43940 23.43940 23.43940 23.43940 23.43940 23.43940
##  [9] 23.43940 23.43940 23.43940 23.43940 23.43940 23.43940
## 
## $四国地方$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 20.58269 18.57733
## 46.00000 19.15641 16.88913
## 46.14286 18.86969 16.45064
## 46.28571 18.59993 16.03808
## 46.42857 18.34444 15.64733
## 46.57143 18.10115 15.27526
## 46.71429 17.86849 14.91942
## 46.85714 17.64515 14.57786
## 47.00000 17.43011 14.24899
## 47.14286 17.22251 13.93149
## 47.28571 17.02162 13.62425
## 47.42857 16.82682 13.32634
## 47.57143 16.63761 13.03696
## 47.71429 16.45351 12.75541
## 
## $四国地方$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 28.15913 30.16449
## 46.00000 27.72238 29.98966
## 46.14286 28.00910 30.42816
## 46.28571 28.27886 30.84072
## 46.42857 28.53436 31.23147
## 46.57143 28.77764 31.60354
## 46.71429 29.01031 31.95937
## 46.85714 29.23364 32.30093
## 47.00000 29.44868 32.62980
## 47.14286 29.65629 32.94731
## 47.28571 29.85718 33.25455
## 47.42857 30.05197 33.55246
## 47.57143 30.24119 33.84184
## 47.71429 30.42528 34.12338
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1]  68.04536  99.42750 113.27624 107.84720  91.61325  91.31192  70.79074
##  [8]  77.44174  94.91159 102.74676  97.31805  92.21242  92.42990  77.98652
## 
## $九州地方$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%         95%
## 45.85714 45.82845  34.0675358
## 46.00000 72.49976  58.2450766
## 46.14286 83.36647  67.5331895
## 46.28571 77.11276  60.8429327
## 46.42857 59.46666  42.4492878
## 46.57143 55.74657  36.9194162
## 46.71429 32.74531  12.6052813
## 46.85714 33.75823  10.6335713
## 47.00000 47.02149  21.6700018
## 47.14286 51.17709  23.8777509
## 47.28571 43.49904  15.0089833
## 47.42857 35.63935   5.6913831
## 47.57143 32.50614   0.7844147
## 47.71429 15.34641 -17.8132571
## 
## $九州地方$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%      95%
## 45.85714  90.26226 102.0232
## 46.00000 126.35523 140.6099
## 46.14286 143.18601 159.0193
## 46.28571 138.58163 154.8515
## 46.42857 123.75983 140.7772
## 46.57143 126.87726 145.7044
## 46.71429 108.83617 128.9762
## 46.85714 121.12525 144.2499
## 47.00000 142.80169 168.1532
## 47.14286 154.31643 181.6158
## 47.28571 151.13705 179.6271
## 47.42857 148.78548 178.7335
## 47.57143 152.35366 184.0754
## 47.71429 140.62663 173.7863

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 233.7388 246.2060 258.0195 274.0600 250.8650 257.4728 247.7903 256.4522
##  [9] 258.2759 258.9549 259.5509 260.0966 260.5963 261.0539
## 
## $北海道$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 222.0240 215.8225
## 46.00000 232.4460 225.1619
## 46.14286 240.3063 230.9295
## 46.28571 255.2812 245.3402
## 46.42857 230.9041 220.3375
## 46.57143 236.2350 224.9924
## 46.71429 225.2004 213.2420
## 46.85714 230.3103 216.4716
## 47.00000 229.6795 214.5414
## 47.14286 227.2763 210.5067
## 47.28571 225.7381 207.8388
## 47.42857 224.1032 205.0495
## 47.57143 222.3914 202.1670
## 47.71429 220.6192 199.2144
## 
## $北海道$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 245.4537 251.6552
## 46.00000 259.9661 267.2502
## 46.14286 275.7327 285.1094
## 46.28571 292.8389 302.7798
## 46.42857 270.8259 281.3925
## 46.57143 278.7105 289.9531
## 46.71429 270.3802 282.3386
## 46.85714 282.5942 296.4329
## 47.00000 286.8723 302.0104
## 47.14286 290.6334 307.4031
## 47.28571 293.3636 311.2630
## 47.42857 296.0900 315.1437
## 47.57143 298.8012 319.0257
## 47.71429 301.4887 322.8935
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 0.4637735 0.4122300 0.4407964 0.4249643 0.4337388 0.4288758 0.4315710
##  [8] 0.4300773 0.4309051 0.4304463 0.4307006 0.4305596 0.4306377 0.4305945
## 
## $青森県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%       95%
## 45.85714 -2.061939 -3.398971
## 46.00000 -2.552358 -4.121716
## 46.14286 -2.604065 -4.215917
## 46.28571 -2.836920 -4.563657
## 46.42857 -2.951753 -4.743924
## 46.57143 -3.116438 -4.993214
## 46.71429 -3.244454 -5.190424
## 46.85714 -3.383874 -5.402858
## 47.00000 -3.509828 -5.595926
## 47.14286 -3.636499 -5.789409
## 47.28571 -3.756830 -5.973575
## 47.42857 -3.875163 -6.154474
## 47.57143 -3.989587 -6.329512
## 47.71429 -4.101527 -6.500687
## 
## $青森県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 2.989486 4.326518
## 46.00000 3.376818 4.946176
## 46.14286 3.485658 5.097510
## 46.28571 3.686848 5.413585
## 46.42857 3.819230 5.611401
## 46.57143 3.974190 5.850966
## 46.71429 4.107596 6.053566
## 46.85714 4.244028 6.263012
## 47.00000 4.371638 6.457737
## 47.14286 4.497391 6.650302
## 47.28571 4.618231 6.834976
## 47.42857 4.736282 7.015594
## 47.57143 4.850862 7.190788
## 47.71429 4.962716 7.361876
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1]  5.392473  5.582780  8.890005 10.927087 10.862762  8.799177  7.210534
##  [8]  7.196649  7.912019  8.679923  8.899219  8.711523  8.386231  8.217640
## 
## $岩手県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 4.012508 3.281999
## 46.00000 3.981942 3.134510
## 46.14286 7.140611 6.214537
## 46.28571 9.171018 8.241411
## 46.42857 9.092933 8.156041
## 46.57143 7.010172 6.063129
## 46.71429 5.358858 4.378640
## 46.85714 5.178060 4.109484
## 47.00000 5.781691 4.653964
## 47.14286 6.472375 5.303770
## 47.28571 6.650371 5.459902
## 47.42857 6.421239 5.208836
## 47.57143 6.048525 4.811018
## 47.71429 5.819993 4.550755
## 
## $岩手県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%       95%
## 45.85714  6.772439  7.502948
## 46.00000  7.183618  8.031050
## 46.14286 10.639399 11.565473
## 46.28571 12.683156 13.612763
## 46.42857 12.632592 13.569484
## 46.57143 10.588183 11.535225
## 46.71429  9.062211 10.042429
## 46.85714  9.215238 10.283814
## 47.00000 10.042347 11.170075
## 47.14286 10.887471 12.056077
## 47.28571 11.148067 12.338535
## 47.42857 11.001806 12.214209
## 47.57143 10.723938 11.961445
## 47.71429 10.615287 11.884524
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 16.43684 15.81435 14.88283 14.97832 15.35229 14.53754 15.50430 14.97249
##  [9] 15.39178 15.02597 15.31626 15.10949 15.23558 15.17989
## 
## $宮城県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%      95%
## 45.85714 11.332975 8.631151
## 46.00000 10.613848 7.860868
## 46.14286  9.473639 6.610188
## 46.28571  9.537489 6.657287
## 46.42857  9.655218 6.639369
## 46.57143  8.813471 5.783334
## 46.71429  9.562088 6.416469
## 46.85714  8.888524 5.667867
## 47.00000  9.126616 5.810037
## 47.14286  8.662348 5.293649
## 47.28571  8.831188 5.398200
## 47.42857  8.494493 4.992725
## 47.57143  8.526484 4.974904
## 47.71429  8.330743 4.705022
## 
## $宮城県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 21.54071 24.24253
## 46.00000 21.01486 23.76784
## 46.14286 20.29202 23.15547
## 46.28571 20.41916 23.29936
## 46.42857 21.04937 24.06522
## 46.57143 20.26160 23.29174
## 46.71429 21.44652 24.59214
## 46.85714 21.05646 24.27712
## 47.00000 21.65695 24.97353
## 47.14286 21.38959 24.75829
## 47.28571 21.80132 25.23431
## 47.42857 21.72449 25.22626
## 47.57143 21.94467 25.49625
## 47.71429 22.02904 25.65476
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 0.3453962 0.3792648 0.3792648 0.3792648 0.3792648 0.3792648 0.3792648
##  [8] 0.3792648 0.3792648 0.3792648 0.3792648 0.3792648 0.3792648 0.3792648
## 
## $秋田県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                 80%       95%
## 45.85714 -0.8782129 -1.525952
## 46.00000 -0.8563562 -1.510454
## 46.14286 -0.8576692 -1.512462
## 46.28571 -0.8589807 -1.514468
## 46.42857 -0.8602909 -1.516472
## 46.57143 -0.8615996 -1.518474
## 46.71429 -0.8629070 -1.520473
## 46.85714 -0.8642130 -1.522470
## 47.00000 -0.8655177 -1.524466
## 47.14286 -0.8668209 -1.526459
## 47.28571 -0.8681229 -1.528450
## 47.42857 -0.8694234 -1.530439
## 47.57143 -0.8707226 -1.532426
## 47.71429 -0.8720205 -1.534411
## 
## $秋田県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 1.569005 2.216745
## 46.00000 1.614886 2.268984
## 46.14286 1.616199 2.270992
## 46.28571 1.617510 2.272998
## 46.42857 1.618820 2.275002
## 46.57143 1.620129 2.277003
## 46.71429 1.621437 2.279003
## 46.85714 1.622743 2.281000
## 47.00000 1.624047 2.282995
## 47.14286 1.625350 2.284988
## 47.28571 1.626652 2.286980
## 47.42857 1.627953 2.288969
## 47.57143 1.629252 2.290956
## 47.71429 1.630550 2.292940
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 1.50071975 0.94595929 0.31069314 0.73252785 0.87497877 0.42708952
##  [7] 0.64670638 0.31068321 0.23937958 0.17965636 0.13108114 0.09306826
## [13] 0.06491069 0.04580986
## 
## $山形県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                  80%         95%
## 45.85714  0.49538334 -0.03680961
## 46.00000 -0.07513956 -0.61567664
## 46.14286 -0.73448318 -1.28776610
## 46.28571 -0.34381559 -0.91359739
## 46.42857 -0.23762676 -0.82660455
## 46.57143 -0.72464353 -1.33433417
## 46.71429 -0.54492296 -1.17573342
## 46.85714 -0.88864475 -1.52353062
## 47.00000 -0.99034544 -1.64132254
## 47.14286 -1.07608622 -1.74083619
## 47.28571 -1.14634917 -1.82257993
## 47.42857 -1.20195329 -1.88749629
## 47.57143 -1.24396791 -1.93684638
## 47.71429 -1.27363572 -1.97210802
## 
## $山形県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 2.506056 3.038249
## 46.00000 1.967058 2.507595
## 46.14286 1.355869 1.909152
## 46.28571 1.808871 2.378653
## 46.42857 1.987584 2.576562
## 46.57143 1.578823 2.188513
## 46.71429 1.838336 2.469146
## 46.85714 1.510011 2.144897
## 47.00000 1.469105 2.120082
## 47.14286 1.435399 2.100149
## 47.28571 1.408511 2.084742
## 47.42857 1.388090 2.073633
## 47.57143 1.373789 2.066668
## 47.71429 1.365255 2.063728
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 4.561964 4.561964 4.561964 4.561964 4.561964 4.561964 4.561964 4.561964
##  [9] 4.561964 4.561964 4.561964 4.561964 4.561964 4.561964
## 
## $福島県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%         95%
## 45.85714 1.927512  0.53291757
## 46.00000 1.861360  0.43174659
## 46.14286 1.796790  0.33299526
## 46.28571 1.733694  0.23649785
## 46.42857 1.671975  0.14210675
## 46.57143 1.611547  0.04968975
## 46.71429 1.552331 -0.04087204
## 46.85714 1.494259 -0.12968607
## 47.00000 1.437266 -0.21684978
## 47.14286 1.381294 -0.30245189
## 47.28571 1.326289 -0.38657343
## 47.42857 1.272205 -0.46928867
## 47.57143 1.218995 -0.55066587
## 47.71429 1.166619 -0.63076794
## 
## $福島県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 7.196416 8.591011
## 46.00000 7.262569 8.692182
## 46.14286 7.327139 8.790933
## 46.28571 7.390235 8.887431
## 46.42857 7.451954 8.981822
## 46.57143 7.512382 9.074239
## 46.71429 7.571597 9.164801
## 46.85714 7.629670 9.253615
## 47.00000 7.686663 9.340778
## 47.14286 7.742635 9.426381
## 47.28571 7.797639 9.510502
## 47.42857 7.851724 9.593217
## 47.57143 7.904934 9.674595
## 47.71429 7.957309 9.754697
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 56.31603 56.30995 46.68577 48.75260 61.37154 54.84349 47.57964 57.19238
##  [9] 57.18364 53.14333 53.55945 58.09759 56.77279 54.24068
## 
## $茨城県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 51.34718 48.71683
## 46.00000 51.06080 48.28207
## 46.14286 41.39602 38.59579
## 46.28571 43.11439 40.12970
## 46.42857 55.09789 51.77682
## 46.57143 48.34322 44.90218
## 46.71429 40.91123 37.38118
## 46.85714 49.56284 45.52401
## 47.00000 49.09131 44.80749
## 47.14286 44.80778 40.39520
## 47.28571 44.87380 40.27590
## 47.42857 48.93957 44.09160
## 47.57143 47.27325 42.24450
## 47.71429 44.45636 39.27685
## 
## $茨城県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 61.28487 63.91522
## 46.00000 61.55910 64.33783
## 46.14286 51.97552 54.77575
## 46.28571 54.39081 57.37550
## 46.42857 67.64519 70.96626
## 46.57143 61.34376 64.78479
## 46.71429 54.24806 57.77810
## 46.85714 64.82192 68.86075
## 47.00000 65.27597 69.55979
## 47.14286 61.47889 65.89146
## 47.28571 62.24509 66.84299
## 47.42857 67.25562 72.10358
## 47.57143 66.27233 71.30108
## 47.71429 64.02501 69.20452
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 7.219724 6.887689 9.121705 7.707366 7.625538 8.872635 6.321098 7.402309
##  [9] 7.402309 7.402309 7.402309 7.402309 7.402309 7.402309
## 
## $栃木県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%       95%
## 45.85714 4.056264 2.3816289
## 46.00000 3.660777 1.9525535
## 46.14286 5.832566 4.0914012
## 46.28571 4.357155 2.5836603
## 46.42857 4.215349 2.4101043
## 46.57143 5.403505 3.5670580
## 46.71429 2.794011 0.9268834
## 46.85714 3.650380 1.6642295
## 47.00000 3.572964 1.5458305
## 47.14286 3.497081 1.4297783
## 47.28571 3.422645 1.3159385
## 47.42857 3.349576 1.2041892
## 47.57143 3.277802 1.0944193
## 47.71429 3.207255 0.9865272
## 
## $栃木県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%      95%
## 45.85714 10.383184 12.05782
## 46.00000 10.114601 11.82282
## 46.14286 12.410844 14.15201
## 46.28571 11.057576 12.83107
## 46.42857 11.035727 12.84097
## 46.57143 12.341766 14.17821
## 46.71429  9.848185 11.71531
## 46.85714 11.154237 13.14039
## 47.00000 11.231654 13.25879
## 47.14286 11.307537 13.37484
## 47.28571 11.381973 13.48868
## 47.42857 11.455041 13.60043
## 47.57143 11.526816 13.71020
## 47.71429 11.597363 13.81809
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 10.565287 10.595145 10.043505  8.925896  9.120176  8.647287  8.627232
##  [8]  8.403327  8.341888  8.223775  8.167786  8.099990  8.058245  8.017092
## 
## $群馬県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%         95%
## 45.85714 5.974753  3.54467070
## 46.00000 5.213301  2.36432447
## 46.14286 4.425972  1.45223023
## 46.28571 3.247079  0.24089459
## 46.42857 3.099438 -0.08774868
## 46.57143 2.542970 -0.68846041
## 46.71429 2.382492 -0.92327334
## 46.85714 2.089933 -1.25217609
## 47.00000 1.952594 -1.42969356
## 47.14286 1.782801 -1.62684506
## 47.28571 1.677318 -1.75852759
## 47.42857 1.569821 -1.88704181
## 47.57143 1.491355 -1.98494588
## 47.71429 1.418114 -2.07517464
## 
## $群馬県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 15.15582 17.58590
## 46.00000 15.97699 18.82597
## 46.14286 15.66104 18.63478
## 46.28571 14.60471 17.61090
## 46.42857 15.14091 18.32810
## 46.57143 14.75160 17.98303
## 46.71429 14.87197 18.17774
## 46.85714 14.71672 18.05883
## 47.00000 14.73118 18.11347
## 47.14286 14.66475 18.07440
## 47.28571 14.65825 18.09410
## 47.42857 14.63016 18.08702
## 47.57143 14.62513 18.10144
## 47.71429 14.61607 18.10936
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 113.1250 130.9277 121.7555 108.2182 145.5679 123.0705 112.7423 120.2078
##  [9] 124.4998 124.2126 120.0623 135.9215 125.2371 118.4073
## 
## $埼玉県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%       95%
## 45.85714  98.35813  90.54102
## 46.00000 115.55792 107.42167
## 46.14286 105.80562  97.36227
## 46.28571  91.70855  82.96889
## 46.42857 128.51690 119.49064
## 46.57143 105.49480  96.19078
## 46.71429  94.65705  85.08332
## 46.85714  99.41591  88.40933
## 47.00000 102.86138  91.40667
## 47.14286 101.75955  89.87360
## 47.28571  96.82310  84.52102
## 47.42857 111.92200  99.21741
## 47.57143 100.50058  87.40585
## 47.71429  92.95515  79.48157
## 
## $埼玉県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 127.8919 135.7090
## 46.00000 146.2974 154.4337
## 46.14286 137.7054 146.1487
## 46.28571 124.7278 133.4675
## 46.42857 162.6189 171.6452
## 46.57143 140.6462 149.9503
## 46.71429 130.8275 140.4012
## 46.85714 140.9998 152.0064
## 47.00000 146.1383 157.5930
## 47.14286 146.6657 158.5517
## 47.28571 143.3015 155.6035
## 47.42857 159.9211 172.6257
## 47.57143 149.9736 163.0684
## 47.71429 143.8595 157.3331
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 76.30526 80.47009 88.84122 84.74659 91.03306 82.13283 79.96544 83.26179
##  [9] 81.53354 87.44493 85.51010 87.31497 83.67038 79.59122
## 
## $千葉県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 64.18525 57.76930
## 46.00000 67.00695 59.88000
## 46.14286 74.82484 67.40502
## 46.28571 70.19800 62.49645
## 46.42857 75.97105 67.99770
## 46.57143 66.57433 58.33816
## 46.71429 63.92582 55.43495
## 46.85714 65.86944 56.66249
## 47.00000 63.36152 53.74183
## 47.14286 68.64757 58.69685
## 47.28571 66.10755 55.83646
## 47.42857 67.32554 56.74378
## 47.57143 63.11082 52.22724
## 47.71429 58.47691 47.29967
## 
## $千葉県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%       95%
## 45.85714  88.42527  94.84122
## 46.00000  93.93322 101.06018
## 46.14286 102.85759 110.27741
## 46.28571  99.29518 106.99674
## 46.42857 106.09507 114.06842
## 46.57143  97.69133 105.92750
## 46.71429  96.00507 104.49594
## 46.85714 100.65415 109.86110
## 47.00000  99.70556 109.32525
## 47.14286 106.24229 116.19301
## 47.28571 104.91265 115.18374
## 47.42857 107.30440 117.88617
## 47.57143 104.22995 115.11352
## 47.71429 100.70553 111.88277
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 432.8981 497.8874 564.1176 540.2778 551.2363 431.0390 366.9146 477.1435
##  [9] 538.7809 604.0982 579.3658 589.4516 468.4010 403.4425
## 
## $東京都$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 385.0989 359.7955
## 46.00000 442.2128 412.7405
## 46.14286 504.9365 473.6080
## 46.28571 477.9292 444.9238
## 46.42857 486.0036 451.4714
## 46.57143 363.1639 327.2330
## 46.71429 296.6066 259.3878
## 46.85714 398.3589 356.6529
## 47.00000 455.1516 410.8809
## 47.14286 516.9444 470.8080
## 47.28571 488.9716 441.1198
## 47.42857 496.0651 446.6292
## 47.57143 372.2413 321.3374
## 47.71429 304.7048 252.4363
## 
## $東京都$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 480.6973 506.0007
## 46.00000 553.5619 583.0342
## 46.14286 623.2986 654.6272
## 46.28571 602.6265 635.6319
## 46.42857 616.4691 651.0013
## 46.57143 498.9141 534.8450
## 46.71429 437.2226 474.4414
## 46.85714 555.9281 597.6342
## 47.00000 622.4103 666.6810
## 47.14286 691.2519 737.3883
## 47.28571 669.7600 717.6118
## 47.42857 682.8381 732.2740
## 47.57143 564.5607 615.4646
## 47.71429 502.1802 554.4488
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 148.6727 197.9055 211.2518 209.8109 201.2717 172.7633 115.4276 172.6194
##  [9] 206.5338 216.8876 217.6725 212.4344 187.2116 138.2075
## 
## $神奈川県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%       95%
## 45.85714 129.03771 118.64358
## 46.00000 175.81190 164.11625
## 46.14286 187.61568 175.10346
## 46.28571 185.91194 173.26061
## 46.42857 176.88493 163.97533
## 46.57143 147.76508 134.53181
## 46.71429  89.53694  75.83127
## 46.85714 143.13429 127.52580
## 47.00000 175.28860 158.74838
## 47.14286 184.26935 167.00226
## 47.28571 184.20765 166.49244
## 47.42857 178.02407 159.80836
## 47.57143 151.80660 133.06433
## 47.71429 101.71829  82.40208
## 
## $神奈川県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 168.3077 178.7018
## 46.00000 219.9991 231.6948
## 46.14286 234.8880 247.4002
## 46.28571 233.7098 246.3611
## 46.42857 225.6585 238.5681
## 46.57143 197.7615 210.9948
## 46.71429 141.3182 155.0239
## 46.85714 202.1046 217.7131
## 47.00000 237.7790 254.3192
## 47.14286 249.5059 266.7730
## 47.28571 251.1373 268.8525
## 47.42857 246.8446 265.0603
## 47.57143 222.6166 241.3588
## 47.71429 174.6967 194.0129
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 10.227885  8.716422  9.355596  9.681324  9.534302  9.464251  9.497993
##  [8]  9.513025  9.505297  9.502079  9.503846  9.504533  9.504130  9.503983
## 
## $新潟県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 7.284776 5.726789
## 46.00000 5.697758 4.099774
## 46.14286 6.336470 4.738241
## 46.28571 6.609402 4.983225
## 46.42857 6.384436 4.716998
## 46.57143 6.261399 4.565911
## 46.71429 6.247706 4.527108
## 46.85714 6.211483 4.463752
## 47.00000 6.152317 4.377356
## 47.14286 6.099449 4.298205
## 47.28571 6.052471 4.225423
## 47.42857 6.004873 4.152265
## 47.57143 5.956804 4.078963
## 47.71429 5.909672 4.006958
## 
## $新潟県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 13.17099 14.72898
## 46.00000 11.73508 13.33307
## 46.14286 12.37472 13.97295
## 46.28571 12.75325 14.37942
## 46.42857 12.68417 14.35161
## 46.57143 12.66710 14.36259
## 46.71429 12.74828 14.46888
## 46.85714 12.81457 14.56230
## 47.00000 12.85828 14.63324
## 47.14286 12.90471 14.70595
## 47.28571 12.95522 14.78227
## 47.42857 13.00419 14.85680
## 47.57143 13.05146 14.92930
## 47.71429 13.09830 15.00101
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 1.806336 1.784589 1.763929 1.744300 1.725652 1.707935 1.691102 1.675111
##  [9] 1.659918 1.645484 1.631771 1.618743 1.606365 1.594606
## 
## $富山県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                 80%       95%
## 45.85714 -0.6451171 -1.942838
## 46.00000 -0.8217268 -2.201427
## 46.14286 -0.9746586 -2.424379
## 46.28571 -1.1084148 -2.618551
## 46.42857 -1.2262891 -2.788952
## 46.57143 -1.3307873 -2.939389
## 46.71429 -1.4238733 -3.072842
## 46.85714 -1.5071235 -3.191697
## 47.00000 -1.5818265 -3.297902
## 47.14286 -1.6490519 -3.393074
## 47.28571 -1.7096987 -3.478566
## 47.42857 -1.7645306 -3.555527
## 47.57143 -1.8142018 -3.624941
## 47.71429 -1.8592768 -3.687652
## 
## $富山県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 4.257789 5.555510
## 46.00000 4.390905 5.770606
## 46.14286 4.502516 5.952236
## 46.28571 4.597015 6.107151
## 46.42857 4.677592 6.240255
## 46.57143 4.746656 6.355259
## 46.71429 4.806078 6.455047
## 46.85714 4.857345 6.541919
## 47.00000 4.901663 6.617739
## 47.14286 4.940020 6.684042
## 47.28571 4.973241 6.742108
## 47.42857 5.002016 6.793013
## 47.57143 5.026932 6.837671
## 47.71429 5.048489 6.876864
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 0.8584226 0.8584226 0.8584226 0.8584226 0.8584226 0.8584226 0.8584226
##  [8] 0.8584226 0.8584226 0.8584226 0.8584226 0.8584226 0.8584226 0.8584226
## 
## $石川県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%       95%
## 45.85714 -2.777482 -4.702213
## 46.00000 -2.981207 -5.013784
## 46.14286 -3.174654 -5.309636
## 46.28571 -3.359238 -5.591932
## 46.42857 -3.536075 -5.862382
## 46.57143 -3.706067 -6.122361
## 46.71429 -3.869951 -6.373000
## 46.85714 -4.028342 -6.615239
## 47.00000 -4.181758 -6.849868
## 47.14286 -4.330640 -7.077564
## 47.28571 -4.475368 -7.298906
## 47.42857 -4.616271 -7.514399
## 47.57143 -4.753638 -7.724484
## 47.71429 -4.887722 -7.929547
## 
## $石川県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 4.494327 6.419059
## 46.00000 4.698052 6.730629
## 46.14286 4.891499 7.026481
## 46.28571 5.076083 7.308777
## 46.42857 5.252920 7.579227
## 46.57143 5.422912 7.839206
## 46.71429 5.586796 8.089846
## 46.85714 5.745187 8.332084
## 47.00000 5.898603 8.566713
## 47.14286 6.047485 8.794409
## 47.28571 6.192213 9.015751
## 47.42857 6.333117 9.231245
## 47.57143 6.470483 9.441329
## 47.71429 6.604567 9.646393
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 1.1899740 1.0550822 1.0394865 1.0262631 1.0150513 1.0055450 0.9974848
##  [8] 0.9906507 0.9848562 0.9799432 0.9757775 0.9722455 0.9692508 0.9667116
## 
## $福井県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                 80%       95%
## 45.85714 -0.8210652 -1.885645
## 46.00000 -1.0932600 -2.230524
## 46.14286 -1.3329151 -2.588789
## 46.28571 -1.4949422 -2.829588
## 46.42857 -1.6079187 -2.996435
## 46.57143 -1.6882095 -3.114197
## 46.71429 -1.7460287 -3.198357
## 46.85714 -1.7880842 -3.259058
## 47.00000 -1.8189261 -3.303159
## 47.14286 -1.8417084 -3.335401
## 47.28571 -1.8586507 -3.359106
## 47.42857 -1.8713324 -3.376632
## 47.57143 -1.8808867 -3.389658
## 47.71429 -1.8881323 -3.399395
## 
## $福井県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 3.201013 4.265593
## 46.00000 3.203424 4.340688
## 46.14286 3.411888 4.667762
## 46.28571 3.547468 4.882114
## 46.42857 3.638021 5.026538
## 46.57143 3.699300 5.125287
## 46.71429 3.740998 5.193327
## 46.85714 3.769386 5.240359
## 47.00000 3.788639 5.272871
## 47.14286 3.801595 5.295287
## 47.28571 3.810206 5.310661
## 47.42857 3.815823 5.321123
## 47.57143 3.819388 5.328160
## 47.71429 3.821556 5.332819
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 6.119761 5.849695 5.766837 5.741415 5.733615 5.731222 5.730488 5.730263
##  [9] 5.730194 5.730172 5.730166 5.730164 5.730163 5.730163
## 
## $山梨県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 4.247709 3.256705
## 46.00000 3.788193 2.696900
## 46.14286 3.632257 2.502279
## 46.28571 3.558087 2.402303
## 46.42857 3.508413 2.330462
## 46.57143 3.466597 2.267776
## 46.71429 3.427613 2.208545
## 46.85714 3.389914 2.151008
## 47.00000 3.353006 2.094599
## 47.14286 3.316723 2.039120
## 47.28571 3.280995 1.984482
## 47.42857 3.245786 1.930636
## 47.57143 3.211072 1.877545
## 47.71429 3.176829 1.825176
## 
## $山梨県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 7.991812 8.982816
## 46.00000 7.911197 9.002490
## 46.14286 7.901416 9.031394
## 46.28571 7.924743 9.080526
## 46.42857 7.958817 9.136768
## 46.57143 7.995848 9.194668
## 46.71429 8.033363 9.252431
## 46.85714 8.070612 9.309518
## 47.00000 8.107381 9.365788
## 47.14286 8.143622 9.421225
## 47.28571 8.179337 9.475850
## 47.42857 8.214542 9.529692
## 47.57143 8.249255 9.582782
## 47.71429 8.283497 9.635150
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 16.60412 16.60412 16.60412 16.60412 16.60412 16.60412 16.60412 16.60412
##  [9] 16.60412 16.60412 16.60412 16.60412 16.60412 16.60412
## 
## $長野県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%       95%
## 45.85714 13.24902 11.472937
## 46.00000 13.00988 11.107210
## 46.14286 12.78569 10.764342
## 46.28571 12.57396 10.440518
## 46.42857 12.37280 10.132878
## 46.57143 12.18079  9.839213
## 46.71429 11.99676  9.557777
## 46.85714 11.81982  9.287158
## 47.00000 11.64918  9.026197
## 47.14286 11.48423  8.773928
## 47.28571 11.32443  8.529537
## 47.42857 11.16933  8.292328
## 47.57143 11.01853  8.061704
## 47.71429 10.87170  7.837145
## 
## $長野県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 19.95922 21.73530
## 46.00000 20.19836 22.10103
## 46.14286 20.42255 22.44390
## 46.28571 20.63428 22.76772
## 46.42857 20.83544 23.07536
## 46.57143 21.02746 23.36903
## 46.71429 21.21148 23.65046
## 46.85714 21.38843 23.92108
## 47.00000 21.55906 24.18204
## 47.14286 21.72401 24.43431
## 47.28571 21.88381 24.67870
## 47.42857 22.03891 24.91591
## 47.57143 22.18971 25.14654
## 47.71429 22.33654 25.37110
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 14.01011 15.65019 15.46492 14.87312 16.46818 13.01459 14.36207 14.49444
##  [9] 14.52457 14.53143 14.53299 14.53335 14.53343 14.53345
## 
## $岐阜県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%      95%
## 45.85714 10.441006 8.551634
## 46.00000 11.640254 9.517523
## 46.14286 11.207147 8.953220
## 46.28571 10.409107 8.045999
## 46.42857 11.812906 9.348551
## 46.57143  8.176884 5.615956
## 46.71429  9.348841 6.695000
## 46.85714  9.043882 6.158532
## 47.00000  8.803968 5.775664
## 47.14286  8.578929 5.427864
## 47.28571  8.362814 5.096519
## 47.42857  8.154123 4.777164
## 47.57143  7.952059 4.468091
## 47.71429  7.756017 4.168262
## 
## $岐阜県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 17.57922 19.46859
## 46.00000 19.66012 21.78285
## 46.14286 19.72269 21.97662
## 46.28571 19.33714 21.70025
## 46.42857 21.12346 23.58781
## 46.57143 17.85230 20.41323
## 46.71429 19.37529 22.02913
## 46.85714 19.94499 22.83034
## 47.00000 20.24517 23.27348
## 47.14286 20.48393 23.63500
## 47.28571 20.70317 23.96947
## 47.42857 20.91258 24.28953
## 47.57143 21.11480 24.59877
## 47.71429 21.31088 24.89863
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 48.49559 68.75340 61.91315 61.29135 60.18839 56.12621 57.89866 58.24831
##  [9] 58.18400 58.18400 58.18400 58.18400 58.18400 58.18400
## 
## $静岡県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 41.89851 38.40623
## 46.00000 61.16755 57.15185
## 46.14286 53.85912 49.59557
## 46.28571 52.79489 48.29713
## 46.42857 51.27142 46.55107
## 46.57143 46.80770 41.87478
## 46.71429 48.19521 43.05851
## 46.85714 47.48983 41.79463
## 47.00000 46.77168 40.73036
## 47.14286 46.24649 39.92716
## 47.28571 45.74346 39.15783
## 47.42857 45.25999 38.41843
## 47.57143 44.79396 37.70571
## 47.71429 44.34362 37.01697
## 
## $静岡県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 55.09267 58.58495
## 46.00000 76.33924 80.35494
## 46.14286 69.96718 74.23073
## 46.28571 69.78781 74.28556
## 46.42857 69.10535 73.82571
## 46.57143 65.44472 70.37764
## 46.71429 67.60212 72.73882
## 46.85714 69.00679 74.70198
## 47.00000 69.59632 75.63764
## 47.14286 70.12151 76.44084
## 47.28571 70.62454 77.21017
## 47.42857 71.10801 77.94957
## 47.57143 71.57404 78.66229
## 47.71429 72.02438 79.35103
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 149.4062 148.7074 197.8202 187.0319 192.6885 151.1790 120.8497 154.5321
##  [9] 154.1003 184.5078 177.8284 181.3306 155.6304 136.8523
## 
## $愛知県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%       95%
## 45.85714 132.79279 123.99820
## 46.00000 128.62346 117.99166
## 46.14286 173.89927 161.23627
## 46.28571 160.06678 145.79230
## 46.42857 162.91552 147.15466
## 46.57143 118.86300 101.75595
## 46.71429  86.17020  67.81196
## 46.85714 112.99384  91.00483
## 47.00000 108.18422  83.87770
## 47.14286 134.17245 107.52652
## 47.28571 123.55930  94.83097
## 47.42857 123.35607  92.66619
## 47.57143  94.18494  61.65770
## 47.71429  72.11829  37.85020
## 
## $愛知県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 166.0196 174.8141
## 46.00000 168.7914 179.4232
## 46.14286 221.7412 234.4042
## 46.28571 213.9971 228.2716
## 46.42857 222.4615 238.2224
## 46.57143 183.4950 200.6020
## 46.71429 155.5293 173.8875
## 46.85714 196.0703 218.0593
## 47.00000 200.0164 224.3230
## 47.14286 234.8432 261.4891
## 47.28571 232.0976 260.8259
## 47.42857 239.3052 269.9951
## 47.57143 217.0758 249.6030
## 47.71429 201.5862 235.8543
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 14.88738 15.87070 16.27635 15.97692 16.35317 15.66675 15.29688 15.62653
##  [9] 15.62653 15.62653 15.62653 15.62653 15.62653 15.62653
## 
## $三重県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%      95%
## 45.85714 10.959658 8.880446
## 46.00000 11.596164 9.333363
## 46.14286 11.681105 9.248532
## 46.28571 11.081943 8.490697
## 46.42857 11.175773 8.435025
## 46.57143 10.221560 7.339052
## 46.71429  9.596473 6.578859
## 46.85714  9.558804 6.346744
## 47.00000  9.281545 5.922713
## 47.14286  9.015904 5.516450
## 47.28571  8.760533 5.125894
## 47.42857  8.514326 4.749352
## 47.57143  8.276361 4.385416
## 47.71429  8.045862 4.032899
## 
## $三重県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 18.81511 20.89432
## 46.00000 20.14523 22.40803
## 46.14286 20.87159 23.30416
## 46.28571 20.87191 23.46315
## 46.42857 21.53057 24.27132
## 46.57143 21.11193 23.99444
## 46.71429 20.99729 24.01490
## 46.85714 21.69426 24.90632
## 47.00000 21.97152 25.33035
## 47.14286 22.23716 25.73661
## 47.28571 22.49253 26.12717
## 47.42857 22.73873 26.50371
## 47.57143 22.97670 26.86764
## 47.71429 23.20720 27.22016
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 6.650598 6.749474 7.302635 7.368552 7.555895 7.588972 7.653065 7.667870
##  [9] 7.690006 7.696241 7.703954 7.706483 7.709191 7.710192
## 
## $滋賀県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%         95%
## 45.85714 2.747052  0.68063939
## 46.00000 2.769855  0.66317282
## 46.14286 2.900816  0.57063349
## 46.28571 2.873656  0.49420124
## 46.42857 2.898704  0.43333674
## 46.57143 2.842696  0.33016889
## 46.71429 2.802700  0.23507215
## 46.85714 2.734004  0.12217330
## 47.00000 2.669984  0.01254411
## 47.14286 2.596935 -0.10247434
## 47.28571 2.525618 -0.21562773
## 47.42857 2.451965 -0.32960865
## 47.57143 2.379371 -0.44206557
## 47.71429 2.306638 -0.55383071
## 
## $滋賀県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 10.55414 12.62056
## 46.00000 10.72909 12.83577
## 46.14286 11.70445 14.03464
## 46.28571 11.86345 14.24290
## 46.42857 12.21309 14.67845
## 46.57143 12.33525 14.84777
## 46.71429 12.50343 15.07106
## 46.85714 12.60174 15.21357
## 47.00000 12.71003 15.36747
## 47.14286 12.79555 15.49496
## 47.28571 12.88229 15.62353
## 47.42857 12.96100 15.74257
## 47.57143 13.03901 15.86045
## 47.71429 13.11375 15.97421
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 26.87252 27.84533 22.77585 25.75056 27.04314 24.61357 21.88060 29.56562
##  [9] 28.21068 23.64952 25.11068 27.03132 25.04686 23.71111
## 
## $京都府$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%       95%
## 45.85714 19.98302 16.335942
## 46.00000 20.34243 16.370639
## 46.14286 15.13247 11.086305
## 46.28571 17.96923 13.850048
## 46.42857 19.12627 14.935331
## 46.57143 16.56343 12.301951
## 46.71429 13.69937  9.368494
## 46.85714 20.91983 16.343019
## 47.00000 19.32852 14.626593
## 47.14286 14.59819  9.806701
## 47.28571 15.89327 11.013872
## 47.42857 17.65078 12.685022
## 47.57143 15.50597 10.455325
## 47.71429 14.01253  8.878407
## 
## $京都府$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 33.76202 37.40910
## 46.00000 35.34822 39.32002
## 46.14286 30.41922 34.46539
## 46.28571 33.53189 37.65108
## 46.42857 34.96001 39.15095
## 46.57143 32.66370 36.92519
## 46.71429 30.06183 34.39271
## 46.85714 38.21141 42.78822
## 47.00000 37.09283 41.79476
## 47.14286 32.70085 37.49234
## 47.28571 34.32809 39.20749
## 47.42857 36.41187 41.37763
## 47.57143 34.58774 39.63839
## 47.71429 33.40970 38.54382
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 411.5425 415.0880 460.9271 483.4941 515.2289 568.1202 421.4349 512.7906
##  [9] 515.2909 547.6175 563.5321 585.9120 623.2119 519.7669
## 
## $大阪府$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 381.6605 365.8419
## 46.00000 381.4552 363.6512
## 46.14286 425.0675 406.0846
## 46.28571 445.5380 425.4453
## 46.42857 475.2862 454.1419
## 46.57143 526.2852 504.1391
## 46.71429 377.7895 354.6851
## 46.85714 457.7628 428.6329
## 47.00000 455.5239 423.8851
## 47.14286 484.1976 450.6252
## 47.28571 496.6586 461.2580
## 47.42857 515.7548 478.6157
## 47.57143 549.9178 511.1183
## 47.71429 443.4648 403.0729
## 
## $大阪府$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 441.4245 457.2431
## 46.00000 448.7207 466.5248
## 46.14286 496.7867 515.7697
## 46.28571 521.4502 541.5429
## 46.42857 555.1715 576.3159
## 46.57143 609.9552 632.1013
## 46.71429 465.0803 488.1848
## 46.85714 567.8184 596.9483
## 47.00000 575.0579 606.6967
## 47.14286 611.0373 644.6098
## 47.28571 630.4056 665.8062
## 47.42857 656.0693 693.2083
## 47.57143 696.5060 735.3056
## 47.71429 596.0690 636.4609
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 141.9295 139.2866 158.4477 157.7869 172.3229 163.0727 122.1078 165.0083
##  [9] 163.2621 175.9223 175.4857 185.0900 178.9782 151.9116
## 
## $兵庫県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%       95%
## 45.85714 131.3673 125.77601
## 46.00000 127.9119 121.89055
## 46.14286 146.3148 139.89204
## 46.28571 144.9406 138.14009
## 46.42857 158.8006 151.64233
## 46.57143 148.9067 141.40771
## 46.71429 107.3261  99.50112
## 46.85714 146.4625 136.64491
## 47.00000 143.4352 132.93954
## 47.14286 154.8923 143.75969
## 47.28571 153.3178 141.58282
## 47.42857 161.8398 149.53186
## 47.57143 154.6939 141.83852
## 47.71429 126.6354 113.25504
## 
## $兵庫県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 152.4917 158.0830
## 46.00000 150.6613 156.6827
## 46.14286 170.5805 177.0033
## 46.28571 170.6333 177.4338
## 46.42857 185.8452 193.0035
## 46.57143 177.2387 184.7378
## 46.71429 136.8895 144.7144
## 46.85714 183.5542 193.3718
## 47.00000 183.0890 193.5847
## 47.14286 196.9523 208.0849
## 47.28571 197.6537 209.3886
## 47.42857 208.3402 220.6482
## 47.57143 203.2625 216.1179
## 47.71429 177.1878 190.5682
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 18.96091 21.33951 21.90080 20.58220 20.45038 19.35836 20.52048 20.28406
##  [9] 20.28406 20.28406 20.28406 20.28406 20.28406 20.28406
## 
## $奈良県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 14.66931 12.39748
## 46.00000 16.86536 14.49690
## 46.14286 17.25127 14.78996
## 46.28571 15.76367 13.21289
## 46.42857 15.46857 12.83136
## 46.57143 14.21846 11.49756
## 46.71429 15.22721 12.42512
## 46.85714 14.68055 11.71424
## 47.00000 14.49996 11.43804
## 47.14286 14.32484 11.17021
## 47.28571 14.15471 10.91003
## 47.42857 13.98919 10.65688
## 47.57143 13.82790 10.41022
## 47.71429 13.67055 10.16957
## 
## $奈良県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 23.25251 25.52435
## 46.00000 25.81365 28.18212
## 46.14286 26.55033 29.01164
## 46.28571 25.40074 27.95151
## 46.42857 25.43218 28.06939
## 46.57143 24.49826 27.21915
## 46.71429 25.81375 28.61584
## 46.85714 25.88757 28.85389
## 47.00000 26.06817 29.13009
## 47.14286 26.24329 29.39792
## 47.28571 26.41341 29.65810
## 47.42857 26.57894 29.91125
## 47.57143 26.74022 30.15791
## 47.71429 26.89758 30.39856
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 9.833508 8.152779 8.277955 7.961385 9.121400 8.782723 8.567266 8.280597
##  [9] 8.841816 8.361393 8.772651 8.420601 8.721968 8.463987
## 
## $和歌山県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 7.773711 6.683321
## 46.00000 5.899550 4.706763
## 46.14286 5.950349 4.718188
## 46.28571 5.477924 4.163260
## 46.42857 6.560662 5.205090
## 46.57143 6.090479 4.665292
## 46.71429 5.796700 4.330051
## 46.85714 5.458680 3.964847
## 47.00000 5.965587 4.443004
## 47.14286 5.398700 3.830345
## 47.28571 5.754006 4.156031
## 47.42857 5.323758 3.684389
## 47.57143 5.568526 3.899195
## 47.71429 5.238567 3.531133
## 
## $和歌山県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 11.89331 12.98370
## 46.00000 10.40601 11.59879
## 46.14286 10.60556 11.83772
## 46.28571 10.44485 11.75951
## 46.42857 11.68214 13.03771
## 46.57143 11.47497 12.90015
## 46.71429 11.33783 12.80448
## 46.85714 11.10251 12.59635
## 47.00000 11.71804 13.24063
## 47.14286 11.32409 12.89244
## 47.28571 11.79130 13.38927
## 47.42857 11.51744 13.15681
## 47.57143 11.87541 13.54474
## 47.71429 11.68941 13.39684
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 0.1790259 0.2026474 0.2278550 0.2612196 0.2723113 0.2817109 0.2893748
##  [8] 0.2932263 0.2961043 0.2981205 0.2993252 0.3001689 0.3007344 0.3010956
## 
## $鳥取県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                 80%       95%
## 45.85714 -0.7516426 -1.244309
## 46.00000 -0.7376610 -1.235430
## 46.14286 -0.7234250 -1.227002
## 46.28571 -0.7084760 -1.221802
## 46.42857 -0.7002375 -1.215074
## 46.57143 -0.6930989 -1.209132
## 46.71429 -0.6871552 -1.204099
## 46.85714 -0.6840666 -1.201414
## 47.00000 -0.6817726 -1.199429
## 47.14286 -0.6802013 -1.198093
## 47.28571 -0.6793264 -1.197393
## 47.42857 -0.6787673 -1.196985
## 47.57143 -0.6784538 -1.196805
## 47.71429 -0.6783218 -1.196794
## 
## $鳥取県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 1.109694 1.602361
## 46.00000 1.142956 1.640725
## 46.14286 1.179135 1.682712
## 46.28571 1.230915 1.744241
## 46.42857 1.244860 1.759696
## 46.57143 1.256521 1.772554
## 46.71429 1.265905 1.782849
## 46.85714 1.270519 1.787867
## 47.00000 1.273981 1.791638
## 47.14286 1.276442 1.794334
## 47.28571 1.277977 1.796044
## 47.42857 1.279105 1.797323
## 47.57143 1.279923 1.798273
## 47.71429 1.280513 1.798985
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 0.4522293 0.4522293 0.4522293 0.4522293 0.4522293 0.4522293 0.4522293
##  [8] 0.4522293 0.4522293 0.4522293 0.4522293 0.4522293 0.4522293 0.4522293
## 
## $島根県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%       95%
## 45.85714 -6.232383 -9.771003
## 46.00000 -6.232383 -9.771003
## 46.14286 -6.232383 -9.771003
## 46.28571 -6.232383 -9.771003
## 46.42857 -6.232383 -9.771003
## 46.57143 -6.232383 -9.771003
## 46.71429 -6.232383 -9.771003
## 46.85714 -6.232383 -9.771003
## 47.00000 -6.232383 -9.771003
## 47.14286 -6.232383 -9.771003
## 47.28571 -6.232383 -9.771003
## 47.42857 -6.232383 -9.771003
## 47.57143 -6.232383 -9.771003
## 47.71429 -6.232383 -9.771003
## 
## $島根県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 7.136842 10.67546
## 46.00000 7.136842 10.67546
## 46.14286 7.136842 10.67546
## 46.28571 7.136842 10.67546
## 46.42857 7.136842 10.67546
## 46.57143 7.136842 10.67546
## 46.71429 7.136842 10.67546
## 46.85714 7.136842 10.67546
## 47.00000 7.136842 10.67546
## 47.14286 7.136842 10.67546
## 47.28571 7.136842 10.67546
## 47.42857 7.136842 10.67546
## 47.57143 7.136842 10.67546
## 47.71429 7.136842 10.67546
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 14.02692 16.53258 14.13786 13.33512 16.49098 12.75813 14.40485 14.19079
##  [9] 12.43748 13.69088 13.92617 12.14061 14.02397 14.07924
## 
## $岡山県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%       95%
## 45.85714 10.857715  9.180042
## 46.00000 13.126293 11.323111
## 46.14286 10.649459  8.802811
## 46.28571  9.766497  7.877384
## 46.42857 12.843898 10.913252
## 46.57143  9.034249  7.062946
## 46.71429 10.605716  8.594577
## 46.85714 10.131588  7.982774
## 47.00000  8.252273  6.036760
## 47.14286  9.407207  7.139569
## 47.28571  9.546245  7.227654
## 47.42857  7.666506  5.298057
## 47.57143  9.457622  7.040344
## 47.71429  9.422476  6.957336
## 
## $岡山県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 17.19612 18.87379
## 46.00000 19.93888 21.74206
## 46.14286 17.62626 19.47291
## 46.28571 16.90374 18.79285
## 46.42857 20.13805 22.06870
## 46.57143 16.48201 18.45331
## 46.71429 18.20398 20.21512
## 46.85714 18.25000 20.39881
## 47.00000 16.62268 18.83819
## 47.14286 17.97455 20.24218
## 47.28571 18.30609 20.62468
## 47.42857 16.61472 18.98317
## 47.57143 18.59032 21.00759
## 47.71429 18.73600 21.20114
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1]  9.721887 10.086613 11.193114 10.827316 10.320428 10.589671 10.805411
##  [8] 10.632719 10.550305 10.652007 10.677703 10.621507 10.617461 10.646868
## 
## $広島県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%         95%
## 45.85714 6.125172  4.22118662
## 46.00000 6.145835  4.05971394
## 46.14286 7.043994  4.84758266
## 46.28571 6.051461  3.52327511
## 46.42857 4.996822  2.17867569
## 46.57143 4.967327  1.99103822
## 46.71429 4.869730  1.72757066
## 46.85714 4.314227  0.96941880
## 47.00000 3.899866  0.37933675
## 47.14286 3.720140  0.05063064
## 47.28571 3.457209 -0.36508965
## 47.42857 3.109697 -0.86681484
## 47.57143 2.836264 -1.28285308
## 47.71429 2.610099 -1.64430889
## 
## $広島県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 13.31860 15.22259
## 46.00000 14.02739 16.11351
## 46.14286 15.34223 17.53865
## 46.28571 15.60317 18.13136
## 46.42857 15.64403 18.46218
## 46.57143 16.21201 19.18830
## 46.71429 16.74109 19.88325
## 46.85714 16.95121 20.29602
## 47.00000 17.20074 20.72127
## 47.14286 17.58387 21.25338
## 47.28571 17.89820 21.72050
## 47.42857 18.13332 22.10983
## 47.57143 18.39866 22.51777
## 47.71429 18.68364 22.93804
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 10.452035 10.892059 12.539994 16.820767 13.664988 12.198154  9.319409
##  [8] 12.496471 11.645246 14.046587 14.095492 13.500599 11.919374 11.650107
## 
## $山口県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%       95%
## 45.85714  7.910827  6.565593
## 46.00000  8.142975  6.687697
## 46.14286  9.663681  8.141052
## 46.28571 13.881981 12.326281
## 46.42857 10.611117  8.994495
## 46.57143  9.082523  7.433208
## 46.71429  6.098479  4.393421
## 46.85714  9.005367  7.157289
## 47.00000  7.984754  6.047007
## 47.14286 10.281859  8.288932
## 47.28571 10.209198  8.151918
## 47.42857  9.514773  7.404804
## 47.57143  7.819718  5.649491
## 47.71429  7.454978  5.234210
## 
## $山口県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 12.99324 14.33848
## 46.00000 13.64114 15.09642
## 46.14286 15.41631 16.93894
## 46.28571 19.75955 21.31525
## 46.42857 16.71886 18.33548
## 46.57143 15.31379 16.96310
## 46.71429 12.54034 14.24540
## 46.85714 15.98757 17.83565
## 47.00000 15.30574 17.24348
## 47.14286 17.81132 19.80424
## 47.28571 17.98179 20.03907
## 47.42857 17.48642 19.59639
## 47.57143 16.01903 18.18926
## 47.71429 15.84524 18.06600
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 0.7456254 0.9929291 0.9950047 1.1741617 0.9128682 0.9756923 0.8338813
##  [8] 0.6634195 0.7847957 0.7842058 0.7109120 0.6973735 0.7904359 0.9093919
## 
## $徳島県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                 80%       95%
## 45.85714 -0.9671839 -1.873890
## 46.00000 -0.8023916 -1.752777
## 46.14286 -0.8187539 -1.778900
## 46.28571 -0.6578493 -1.627657
## 46.42857 -0.9372151 -1.916590
## 46.57143 -0.8922885 -1.881138
## 46.71429 -1.0518271 -2.050061
## 46.85714 -1.3158311 -2.363583
## 47.00000 -1.2279745 -2.293471
## 47.14286 -1.2522464 -2.330279
## 47.28571 -1.3489501 -2.439375
## 47.42857 -1.3856352 -2.488313
## 47.57143 -1.3154652 -2.430262
## 47.71429 -1.2191552 -2.345940
## 
## $徳島県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 2.458435 3.365141
## 46.00000 2.788250 3.738635
## 46.14286 2.808763 3.768909
## 46.28571 3.006173 3.975981
## 46.42857 2.762952 3.742326
## 46.57143 2.843673 3.832522
## 46.71429 2.719590 3.717823
## 46.85714 2.642670 3.690422
## 47.00000 2.797566 3.863062
## 47.14286 2.820658 3.898691
## 47.28571 2.770774 3.861199
## 47.42857 2.780382 3.883061
## 47.57143 2.896337 4.011134
## 47.71429 3.037939 4.164724
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 1.389159 1.411832 1.411832 1.411832 1.411832 1.411832 1.411832 1.411832
##  [9] 1.411832 1.411832 1.411832 1.411832 1.411832 1.411832
## 
## $香川県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                  80%        95%
## 45.85714 -0.04983846 -0.8115978
## 46.00000 -0.08495271 -0.8773028
## 46.14286 -0.09283088 -0.8893514
## 46.28571 -0.10066802 -0.9013373
## 46.42857 -0.10846476 -0.9132614
## 46.57143 -0.11622172 -0.9251246
## 46.71429 -0.12393950 -0.9369280
## 46.85714 -0.13161869 -0.9486723
## 47.00000 -0.13925986 -0.9603584
## 47.14286 -0.14686357 -0.9719873
## 47.28571 -0.15443037 -0.9835597
## 47.42857 -0.16196079 -0.9950765
## 47.57143 -0.16945534 -1.0065384
## 47.71429 -0.17691455 -1.0179463
## 
## $香川県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 2.828157 3.589916
## 46.00000 2.908617 3.700967
## 46.14286 2.916495 3.713016
## 46.28571 2.924333 3.725002
## 46.42857 2.932129 3.736926
## 46.57143 2.939886 3.748789
## 46.71429 2.947604 3.760592
## 46.85714 2.955283 3.772337
## 47.00000 2.962924 3.784023
## 47.14286 2.970528 3.795652
## 47.28571 2.978095 3.807224
## 47.42857 2.985625 3.818741
## 47.57143 2.993120 3.830203
## 47.71429 3.000579 3.841611
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 22.989408 20.345564 18.023791 15.984854 14.194298 12.621867 11.240987
##  [8] 10.028326  8.963389  8.028182  7.206902  6.485669  5.852296  5.296080
## 
## $愛媛県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                 80%          95%
## 45.85714 20.6666923 19.437120776
## 46.00000 17.2543427 15.617949050
## 46.14286 14.4510688 12.559784126
## 46.28571 12.0811516 10.014656526
## 46.42857 10.0533725  7.861298801
## 46.57143  8.3068917  6.022681978
## 46.71429  6.7964380  4.443635676
## 46.85714  5.4863721  3.082007225
## 47.00000  4.3477212  1.904334204
## 47.14286  3.3564600  0.883399577
## 47.28571  2.4924052 -0.003298467
## 47.42857  1.7384480 -0.774578873
## 47.57143  1.0799912 -1.446314157
## 47.71429  0.5045202 -2.031978111
## 
## $愛媛県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 25.31212 26.54170
## 46.00000 23.43679 25.07318
## 46.14286 21.59651 23.48780
## 46.28571 19.88856 21.95505
## 46.42857 18.33522 20.52730
## 46.57143 16.93684 19.22105
## 46.71429 15.68554 18.03834
## 46.85714 14.57028 16.97464
## 47.00000 13.57906 16.02244
## 47.14286 12.69990 15.17297
## 47.28571 11.92140 14.41710
## 47.42857 11.23289 13.74592
## 47.57143 10.62460 13.15091
## 47.71429 10.08764 12.62414
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 0.6606066 0.6455835 0.6317267 0.6189456 0.6071568 0.5962832 0.5862537
##  [8] 0.5770029 0.5684702 0.5606000 0.5533407 0.5466450 0.5404691 0.5347727
## 
## $高知県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%       95%
## 45.85714 -1.022248 -1.913097
## 46.00000 -1.058893 -1.961189
## 46.14286 -1.090932 -2.002852
## 46.28571 -1.119032 -2.039061
## 46.42857 -1.143747 -2.070620
## 46.57143 -1.165544 -2.098199
## 46.71429 -1.184814 -2.122360
## 46.85714 -1.201888 -2.143575
## 47.00000 -1.217049 -2.162246
## 47.14286 -1.230539 -2.178711
## 47.28571 -1.242566 -2.193261
## 47.42857 -1.253308 -2.206145
## 47.57143 -1.262919 -2.217574
## 47.71429 -1.271532 -2.227732
## 
## $高知県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 2.343461 3.234310
## 46.00000 2.350060 3.252356
## 46.14286 2.354385 3.266306
## 46.28571 2.356923 3.276952
## 46.42857 2.358061 3.284934
## 46.57143 2.358111 3.290766
## 46.71429 2.357321 3.294867
## 46.85714 2.355893 3.297581
## 47.00000 2.353989 3.299186
## 47.14286 2.351739 3.299911
## 47.28571 2.349247 3.299943
## 47.42857 2.346598 3.299435
## 47.57143 2.343857 3.298513
## 47.71429 2.341078 3.297278
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 17.66221 22.49842 23.64430 28.30719 27.05875 25.54227 18.56130 22.47671
##  [9] 25.37615 25.38855 28.66592 27.78844 26.72256 21.81590
## 
## $福岡県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                    80%         95%
## 45.85714  3.876446e+00  -3.4212974
## 46.00000  6.202187e+00  -2.4245162
## 46.14286  5.561741e+00  -4.0105859
## 46.28571  9.158279e+00  -0.9785404
## 46.42857  6.899815e+00  -3.7716788
## 46.57143  4.421561e+00  -6.7590677
## 46.71429 -3.479257e+00 -15.1468237
## 46.85714 -1.959923e+00 -14.8958935
## 47.00000 -6.586701e-01 -14.4406688
## 47.14286 -2.067131e+00 -16.6012895
## 47.28571 -8.427655e-04 -15.1761095
## 47.42857 -2.040276e+00 -17.8306423
## 47.57143 -4.224505e+00 -20.6068926
## 47.71429 -1.021049e+01 -27.1642392
## 
## $福岡県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 31.44798 38.74572
## 46.00000 38.79465 47.42135
## 46.14286 41.72685 51.29918
## 46.28571 47.45609 57.59291
## 46.42857 47.21768 57.88917
## 46.57143 46.66298 57.84361
## 46.71429 40.60186 52.26943
## 46.85714 46.91334 59.84931
## 47.00000 51.41096 65.19296
## 47.14286 52.84423 67.37839
## 47.28571 57.33268 72.50795
## 47.42857 57.61715 73.40752
## 47.57143 57.66963 74.05202
## 47.71429 53.84230 70.79605
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 1.25996 1.25996 1.25996 1.25996 1.25996 1.25996 1.25996 1.25996 1.25996
## [10] 1.25996 1.25996 1.25996 1.25996 1.25996
## 
## $佐賀県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                 80%       95%
## 45.85714 -0.7689803 -1.843036
## 46.00000 -0.8420046 -1.954717
## 46.14286 -0.9125757 -2.062647
## 46.28571 -0.9809255 -2.167179
## 46.42857 -1.0472513 -2.268615
## 46.57143 -1.1117230 -2.367216
## 46.71429 -1.1744879 -2.463207
## 46.85714 -1.2356748 -2.556784
## 47.00000 -1.2953970 -2.648121
## 47.14286 -1.3537549 -2.737372
## 47.28571 -1.4108380 -2.824673
## 47.42857 -1.4667263 -2.910147
## 47.57143 -1.5214919 -2.993904
## 47.71429 -1.5751998 -3.076043
## 
## $佐賀県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 3.288900 4.362956
## 46.00000 3.361925 4.474637
## 46.14286 3.432496 4.582567
## 46.28571 3.500845 4.687099
## 46.42857 3.567171 4.788535
## 46.57143 3.631643 4.887136
## 46.71429 3.694408 4.983127
## 46.85714 3.755595 5.076704
## 47.00000 3.815317 5.168041
## 47.14286 3.873675 5.257292
## 47.28571 3.930758 5.344593
## 47.42857 3.986646 5.430067
## 47.57143 4.041412 5.513824
## 47.71429 4.095120 5.595963
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 1.092481 1.141688 1.074435 1.479327 1.502020 1.219250 1.124485 1.235882
##  [9] 1.244731 1.228488 1.440417 1.503932 1.217634 1.241644
## 
## $長崎県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%       95%
## 45.85714 -1.021641 -2.140790
## 46.00000 -1.212222 -2.458307
## 46.14286 -1.763825 -3.266309
## 46.28571 -1.644455 -3.298085
## 46.42857 -1.941129 -3.763822
## 46.57143 -2.488283 -4.450933
## 46.71429 -2.842946 -4.943177
## 46.85714 -3.117703 -5.422352
## 47.00000 -3.393033 -5.848118
## 47.14286 -3.710183 -6.324557
## 47.28571 -3.765638 -6.521557
## 47.42857 -3.964267 -6.858957
## 47.57143 -4.496866 -7.521940
## 47.71429 -4.710849 -7.861909
## 
## $長崎県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%       95%
## 45.85714 3.206604  4.325753
## 46.00000 3.495598  4.741682
## 46.14286 3.912695  5.415179
## 46.28571 4.603108  6.256738
## 46.42857 4.945169  6.767862
## 46.57143 4.926783  6.889432
## 46.71429 5.091915  7.192146
## 46.85714 5.589467  7.894116
## 47.00000 5.882496  8.337580
## 47.14286 6.167160  8.781534
## 47.28571 6.646472  9.402391
## 47.42857 6.972131  9.866821
## 47.57143 6.932134  9.957207
## 47.71429 7.194137 10.345197
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 7.470842 6.278357 6.184429 6.147943 6.114633 6.084221 6.056456 6.031107
##  [9] 6.007964 5.986835 5.967545 5.949933 5.933854 5.919175
## 
## $熊本県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                  80%        95%
## 45.85714  3.17812070  0.9056911
## 46.00000  0.74538028 -2.1836007
## 46.14286  0.55524879 -2.4246593
## 46.28571  0.22943423 -2.9036353
## 46.42857 -0.04405084 -3.3042610
## 46.57143 -0.27620938 -3.6432178
## 46.71429 -0.47509091 -3.9326828
## 46.85714 -0.64677943 -4.1818388
## 47.00000 -0.79598748 -4.3977817
## 47.14286 -0.92643520 -4.5860993
## 47.28571 -1.04110394 -4.7512584
## 47.42857 -1.14241230 -4.8968732
## 47.57143 -1.23234238 -5.0258977
## 47.71429 -1.31253243 -5.1407669
## 
## $熊本県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 11.76356 14.03599
## 46.00000 11.81133 14.74031
## 46.14286 11.81361 14.79352
## 46.28571 12.06645 15.19952
## 46.42857 12.27332 15.53353
## 46.57143 12.44465 15.81166
## 46.71429 12.58800 16.04559
## 46.85714 12.70899 16.24405
## 47.00000 12.81191 16.41371
## 47.14286 12.90010 16.55977
## 47.28571 12.97619 16.68635
## 47.42857 13.04228 16.79674
## 47.57143 13.10005 16.89361
## 47.71429 13.15088 16.97912
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 4.753832 4.894818 3.332437 4.070427 3.623665 3.407693 3.208487 3.024744
##  [9] 2.855266 2.698943 2.554756 2.421761 2.299091 2.185943
## 
## $大分県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                  80%         95%
## 45.85714  3.03987115  2.13255534
## 46.00000  2.98990894  1.98151079
## 46.14286  1.11795946 -0.05431426
## 46.28571  1.76385077  0.54282324
## 46.42857  1.30677658  0.08028984
## 46.57143  1.05356417 -0.19263648
## 46.71429  0.82313236 -0.43959802
## 46.85714  0.61314262 -0.66348226
## 47.00000  0.42155621 -0.86677172
## 47.14286  0.24658215 -1.05161933
## 47.28571  0.08663745 -1.21990540
## 47.42857 -0.05968401 -1.37328175
## 47.57143 -0.19363648 -1.51320661
## 47.71429 -0.31634260 -1.64097266
## 
## $大分県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 6.467792 7.375108
## 46.00000 6.799728 7.808126
## 46.14286 5.546915 6.719189
## 46.28571 6.377003 7.598030
## 46.42857 5.940554 7.167041
## 46.57143 5.761822 7.008023
## 46.71429 5.593841 6.856572
## 46.85714 5.436346 6.712971
## 47.00000 5.288975 6.577303
## 47.14286 5.151304 6.449506
## 47.28571 5.022874 6.329417
## 47.42857 4.903206 6.216804
## 47.57143 4.791818 6.111388
## 47.71429 4.688229 6.012859
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 11.465864  8.843984 10.100769  8.897090 10.049908  8.945802 10.003254
##  [8]  8.990485  9.960459  9.031471  9.921204  9.069067  9.885197  9.103553
## 
## $宮崎県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%        95%
## 45.85714 8.599090  7.0815112
## 46.00000 5.831743  4.2371592
## 46.14286 6.672110  4.8570874
## 46.28571 5.185548  3.2207769
## 46.42857 5.996789  3.8511982
## 46.57143 4.647828  2.3726173
## 46.71429 5.410047  2.9785496
## 46.85714 4.177438  1.6295651
## 47.00000 4.884486  2.1974283
## 47.14286 3.753723  0.9598519
## 47.28571 4.404685  1.4844160
## 47.42857 3.364621  0.3448694
## 47.57143 3.960899  0.8247644
## 47.71429 3.002398 -0.2273581
## 
## $宮崎県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 14.33264 15.85022
## 46.00000 11.85623 13.45081
## 46.14286 13.52943 15.34445
## 46.28571 12.60863 14.57340
## 46.42857 14.10303 16.24862
## 46.57143 13.24378 15.51899
## 46.71429 14.59646 17.02796
## 46.85714 13.80353 16.35141
## 47.00000 15.03643 17.72349
## 47.14286 14.30922 17.10309
## 47.28571 15.43772 18.35799
## 47.42857 14.77351 17.79326
## 47.57143 15.80950 18.94563
## 47.71429 15.20471 18.43446
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 4.564514 4.310067 4.161398 4.074534 4.023781 3.994126 3.976800 3.966677
##  [9] 3.960762 3.957306 3.955286 3.954106 3.953417 3.953014
## 
## $鹿児島県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                 80%       95%
## 45.85714  0.5015519 -1.649250
## 46.00000 -0.4660593 -2.994388
## 46.14286 -0.8768036 -3.543867
## 46.28571 -1.0768408 -3.803814
## 46.42857 -1.1839764 -3.940796
## 46.57143 -1.2462000 -4.020261
## 46.71429 -1.2852493 -4.070810
## 46.85714 -1.3117819 -4.106029
## 47.00000 -1.3313431 -4.132814
## 47.14286 -1.3469427 -4.154842
## 47.28571 -1.3602609 -4.174142
## 47.42857 -1.3722524 -4.191856
## 47.57143 -1.3834660 -4.208641
## 47.71429 -1.3942189 -4.224873
## 
## $鹿児島県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 8.627476 10.77828
## 46.00000 9.086193 11.61452
## 46.14286 9.199600 11.86666
## 46.28571 9.225909 11.95288
## 46.42857 9.231538 11.98836
## 46.57143 9.234453 12.00851
## 46.71429 9.238849 12.02441
## 46.85714 9.245135 12.03938
## 47.00000 9.252866 12.05434
## 47.14286 9.261554 12.06945
## 47.28571 9.270833 12.08471
## 47.42857 9.280465 12.10007
## 47.57143 9.290300 12.11548
## 47.71429 9.300247 12.13090
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##  [1] 26.11549 29.32003 32.93364 28.51265 30.21465 26.95721 24.24260 26.71132
##  [9] 26.71132 26.71132 26.71132 26.71132 26.71132 26.71132
## 
## $沖縄県$lower
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##                80%        95%
## 45.85714 14.132156   7.788562
## 46.00000 16.272909   9.366181
## 46.14286 18.903156  11.475870
## 46.28571 13.563359   5.649682
## 46.42857 14.399839   6.027982
## 46.57143 10.321843   1.515613
## 46.71429  6.825298  -2.394863
## 46.85714  7.612696  -2.497506
## 47.00000  6.596624  -4.051454
## 47.14286  5.629466  -5.530594
## 47.28571  4.704773  -6.944791
## 47.42857  3.817398  -8.301915
## 47.57143  2.963157  -9.608363
## 47.71429  2.138595 -10.869422
## 
## $沖縄県$upper
## Time Series:
## Start = c(45, 7) 
## End = c(47, 6) 
## Frequency = 7 
##               80%      95%
## 45.85714 38.09883 44.44242
## 46.00000 42.36715 49.27388
## 46.14286 46.96411 54.39140
## 46.28571 43.46194 51.37562
## 46.42857 46.02947 54.40132
## 46.57143 43.59257 52.39880
## 46.71429 41.65990 50.88006
## 46.85714 45.80995 55.92015
## 47.00000 46.82602 57.47410
## 47.14286 47.79318 58.95324
## 47.28571 48.71787 60.36744
## 47.42857 49.60525 61.72456
## 47.57143 50.45949 63.03101
## 47.71429 51.28405 64.29207